Cognitive Middleware Orchestration: A Human-AI Framework for Distributed Data Consistency

Abstract

With the advent of distributed microservices-based database architectures, polyglot persistence (where services are expected to be mostly independent from each other), and heterogeneous data stores, consistency across data stores has become a fundamental problem that existing tools do not adequately solve, particularly for larger context-driven business and compliance processes. This article develops a cognitive middleware orchestration framework for the symbiotic cooperation of artificial intelligence systems and human domain experts for resolving conflicts in data consistency within distributed systems. The proposed framework extends the customary orchestration with a clever middleware layer that observes the data streams and includes machine learning models to detect and classify conflict patterns. In conflict detection, interactive decision interfaces are provided to human experts. Suggested decisions to resolve each conflict are offered via the AI, and the human expert provides business justification for their chosen decision to support adaptive learning engines. As a result, the framework realizes progressive autonomy models, which increase the autonomy of the AI based on the assessment of the AI performance while keeping humans in the loop for the most critical decisions. The contributions advance the theory of consistency management and realize learning-enabled service meshes within cloud-native environments.